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The appropriate model is estimated according to the dependent variable scale type:
In addition to using predictors to estimate a regression model for each class, covariates can be specified to refine class descriptions and improve classification of cases into the appropriate latent classes.
Latent GOLD® improves over traditional ad-hoc types of cluster analysis methods by including model selection criteria and probability-based classification. Posterior membership probabilities are estimated directly from the model parameters and used to assign cases to the classes.
Latent GOLD® is a powerful latent class and finite mixture program with a very user-friendly point-and-click interface (GUI). Two add-on options are available to extend the basic version of the program.
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